MC-DBN: A Deep Belief Network-Based Model for Modality Completion
This addresses the challenge of incomplete multi-modal data for applications like stock forecasting and health monitoring, but it appears incremental as it builds on existing deep belief network methods.
The paper tackles the problem of missing values in multi-modal data for stock market forecasting and heart rate monitoring by proposing MC-DBN, a deep belief network-based model that uses complete data features to fill gaps, resulting in enhanced model performance as shown in evaluations on two datasets.
Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handling missing values in modal data, though they may exhibit limitations in the context of sparse information. Addressing this challenge, we propose a Modality Completion Deep Belief Network-Based Model (MC-DBN). This approach utilizes implicit features of complete data to compensate for gaps between itself and additional incomplete data. It ensures that the enhanced multi-modal data closely aligns with the dynamic nature of the real world to enhance the effectiveness of the model. We conduct evaluations of the MC-DBN model in two datasets from the stock market forecasting and heart rate monitoring domains. Comprehensive experiments showcase the model's capacity to bridge the semantic divide present in multi-modal data, subsequently enhancing its performance. The source code is available at: https://github.com/logan-0623/DBN-generate